"use strict";

Object.defineProperty(exports, "__esModule", {
  value: true
});
exports.createCsSymperm = void 0;
var _csCumsum = require("./csCumsum.js");
var _factory = require("../../../utils/factory.js");
// Copyright (c) 2006-2024, Timothy A. Davis, All Rights Reserved.
// SPDX-License-Identifier: LGPL-2.1+
// https://github.com/DrTimothyAldenDavis/SuiteSparse/tree/dev/CSparse/Source

const name = 'csSymperm';
const dependencies = ['conj', 'SparseMatrix'];
const createCsSymperm = exports.createCsSymperm = /* #__PURE__ */(0, _factory.factory)(name, dependencies, _ref => {
  let {
    conj,
    SparseMatrix
  } = _ref;
  /**
   * Computes the symmetric permutation of matrix A accessing only
   * the upper triangular part of A.
   *
   * C = P * A * P'
   *
   * @param {Matrix}  a               The A matrix
   * @param {Array}   pinv            The inverse of permutation vector
   * @param {boolean} values          Process matrix values (true)
   *
   * @return {Matrix}                 The C matrix, C = P * A * P'
   */
  return function csSymperm(a, pinv, values) {
    // A matrix arrays
    const avalues = a._values;
    const aindex = a._index;
    const aptr = a._ptr;
    const asize = a._size;
    // columns
    const n = asize[1];
    // C matrix arrays
    const cvalues = values && avalues ? [] : null;
    const cindex = []; // (nz)
    const cptr = []; // (n + 1)
    // variables
    let i, i2, j, j2, p, p0, p1;
    // create workspace vector
    const w = []; // (n)
    // count entries in each column of C
    for (j = 0; j < n; j++) {
      // column j of A is column j2 of C
      j2 = pinv ? pinv[j] : j;
      // loop values in column j
      for (p0 = aptr[j], p1 = aptr[j + 1], p = p0; p < p1; p++) {
        // row
        i = aindex[p];
        // skip lower triangular part of A
        if (i > j) {
          continue;
        }
        // row i of A is row i2 of C
        i2 = pinv ? pinv[i] : i;
        // column count of C
        w[Math.max(i2, j2)]++;
      }
    }
    // compute column pointers of C
    (0, _csCumsum.csCumsum)(cptr, w, n);
    // loop columns
    for (j = 0; j < n; j++) {
      // column j of A is column j2 of C
      j2 = pinv ? pinv[j] : j;
      // loop values in column j
      for (p0 = aptr[j], p1 = aptr[j + 1], p = p0; p < p1; p++) {
        // row
        i = aindex[p];
        // skip lower triangular part of A
        if (i > j) {
          continue;
        }
        // row i of A is row i2 of C
        i2 = pinv ? pinv[i] : i;
        // C index for column j2
        const q = w[Math.max(i2, j2)]++;
        // update C index for entry q
        cindex[q] = Math.min(i2, j2);
        // check we need to process values
        if (cvalues) {
          cvalues[q] = i2 <= j2 ? avalues[p] : conj(avalues[p]);
        }
      }
    }
    // return C matrix
    return new SparseMatrix({
      values: cvalues,
      index: cindex,
      ptr: cptr,
      size: [n, n]
    });
  };
});